Ship-generated wave-induced sediment dynamics in upper St. Lawrence River: analysis of field data
Bibliographic record
Abstract
Abstract This study examines the effects of combined ship-generated waves and wind-driven waves on sediment dynamics in the upper St. Lawrence River, focusing on two sites at Mariatown and Jacobs Island in Ontario, Canada. Six loggers installed at the two study sites recorded wave and turbidity data over a period of 300 days to distinguish the influences of natural versus ship-induced waves on sediment resuspension. The findings indicate that wind-driven waves regularly resuspend sediment, establishing a baseline turbidity level, while ship-generated waves cause short-lived but intense turbidity spikes, particularly in shallow zones with fine sediment. Spectral signal analyses, including synthetic natural wave modeling and frequency-based filtering, are used to isolate the frequency characteristics of primary and secondary ship-generated waves from those of natural wind waves, enabling a focused assessment of each wave type’s impact on sediment dynamics. Wavelet analysis is applied as a validation tool to confirm the spectral localization of separated wave components. The study demonstrates that factors such as water depth, sediment type, and proximity to the navigation channel strongly influence local turbidity responses. While historical shoreline imagery is used to provide spatial context, shoreline change was not formally analyzed in this study. Overall, the results offer a replicable framework for isolating wave contributions to sediment resuspension and support future efforts in sustainable sediment and shoreline management in modified river environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".